// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include <include/ocr_det.h>

namespace PaddleOCR
{

    void DBDetector::LoadModel(const std::string &model_dir)
    {
        //   AnalysisConfig config;
        paddle_infer::Config config;
        config.SetModel(model_dir + "/inference.pdmodel",
                        model_dir + "/inference.pdiparams");

        if (this->use_gpu_)
        {
            config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
            if (this->use_tensorrt_)
            {
                auto precision = paddle_infer::Config::Precision::kFloat32;
                if (this->precision_ == "fp16")
                {
                    precision = paddle_infer::Config::Precision::kHalf;
                }
                if (this->precision_ == "int8")
                {
                    precision = paddle_infer::Config::Precision::kInt8;
                }
                config.EnableTensorRtEngine(1 << 30, 1, 20, precision, false, false);
                if (!Utility::PathExists("./trt_det_shape.txt"))
                {
                    config.CollectShapeRangeInfo("./trt_det_shape.txt");
                }
                else
                {
                    config.EnableTunedTensorRtDynamicShape("./trt_det_shape.txt", true);
                }
            }
        }
        else
        {
            config.DisableGpu();
            if (this->use_mkldnn_)
            {
                config.EnableMKLDNN();
                // cache 10 different shapes for mkldnn to avoid memory leak
                config.SetMkldnnCacheCapacity(10);
            }
            else
            {
                config.DisableMKLDNN();
            }
            config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
        }
        // use zero_copy_run as default
        config.SwitchUseFeedFetchOps(false);
        // true for multiple input
        config.SwitchSpecifyInputNames(true);

        config.SwitchIrOptim(true);

        config.EnableMemoryOptim();
        config.DisableGlogInfo();

        this->predictor_ = paddle_infer::CreatePredictor(config);
    }

    void DBDetector::Run(cv::Mat &img,
                         std::vector<std::vector<std::vector<int>>> &boxes,
                         std::vector<double> &times)
    {
        float ratio_h{};
        float ratio_w{};

        cv::Mat srcimg;
        cv::Mat resize_img;
        img.copyTo(srcimg);

        auto preprocess_start = std::chrono::steady_clock::now();
        this->resize_op_.Run(img, resize_img, this->limit_type_,
                             this->limit_side_len_, ratio_h, ratio_w,
                             this->use_tensorrt_);

        this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
                                this->is_scale_);

        std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
        this->permute_op_.Run(&resize_img, input.data());
        auto preprocess_end = std::chrono::steady_clock::now();

        // Inference.
        auto input_names = this->predictor_->GetInputNames();
        auto input_t = this->predictor_->GetInputHandle(input_names[0]);
        input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
        auto inference_start = std::chrono::steady_clock::now();
        input_t->CopyFromCpu(input.data());

        this->predictor_->Run();

        std::vector<float> out_data;
        auto output_names = this->predictor_->GetOutputNames();
        auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
        std::vector<int> output_shape = output_t->shape();
        int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
                                      std::multiplies<int>());

        out_data.resize(out_num);
        output_t->CopyToCpu(out_data.data());
        auto inference_end = std::chrono::steady_clock::now();

        auto postprocess_start = std::chrono::steady_clock::now();
        int n2 = output_shape[2];
        int n3 = output_shape[3];
        int n = n2 * n3;

        std::vector<float> pred(n, 0.0);
        std::vector<unsigned char> cbuf(n, ' ');

        for (int i = 0; i < n; i++)
        {
            pred[i] = float(out_data[i]);
            cbuf[i] = (unsigned char)((out_data[i]) * 255);
        }

        cv::Mat cbuf_map(n2, n3, CV_8UC1, (unsigned char *)cbuf.data());
        cv::Mat pred_map(n2, n3, CV_32F, (float *)pred.data());

        const double threshold = this->det_db_thresh_ * 255;
        const double maxvalue = 255;
        cv::Mat bit_map;
        cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
        if (this->use_dilation_)
        {
            cv::Mat dila_ele =
                cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
            cv::dilate(bit_map, bit_map, dila_ele);
        }

        boxes = post_processor_.BoxesFromBitmap(
            pred_map, bit_map, this->det_db_box_thresh_, this->det_db_unclip_ratio_,
            this->det_db_score_mode_);

        boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);
        auto postprocess_end = std::chrono::steady_clock::now();

        std::chrono::duration<float> preprocess_diff =
            preprocess_end - preprocess_start;
        times.push_back(double(preprocess_diff.count() * 1000));
        std::chrono::duration<float> inference_diff = inference_end - inference_start;
        times.push_back(double(inference_diff.count() * 1000));
        std::chrono::duration<float> postprocess_diff =
            postprocess_end - postprocess_start;
        times.push_back(double(postprocess_diff.count() * 1000));
    }

} // namespace PaddleOCR
